Assessment of the effectiveness of ship machinery noise reduction measures using a test platform in a water basin
Bibliographic record
Abstract
Underwater radiated noise (URN) from commercial shipping is partly responsible for increased ocean ambient noise levels in the last decades. To preserve marine wildlife, there is a need to reduce it. Machinery noise is the dominant URN source at lower speeds. Mitigation technologies exist to reduce it, but a lack of quantitative data regarding their effectiveness results in limited practical ship applications since the cost-to-benefit ratio is imprecise. A small ship-like structure (test platform) representative of a ship section is designed and constructed to conduct measurements in a controlled environment and at a lower cost than actual on-ship testing. The platform is deployed in a water basin whose acoustic response is first characterized by reverberation measurements. Vibroacoustic sources simulate structure-borne and airborne noise, while hydrophones and sensors measure the response in the water basin and of the platform. Measurements with and without standard mitigation technologies installed in the platform are conducted to quantify the insertion loss. Up to 37 and 20 dB URN reductions are obtained with elastic mounts and mineral wool, respectively. The results obtained with the platform and the developed methodology can support and guide the implementation of mitigation measures in current and future ship constructions. • A small test platform representative of a ship section is deployed in a water basin. • The basin’s acoustic response is characterized by reverberation measurements. • Ship machinery noise control measures’ insertion loss is assessed with the platform. • Up to 37 and 20 dB URN reductions are achieved with elastic mounts and mineral wool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".